DocumentCode :
9704
Title :
A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation
Author :
Korez, Robert ; Ibragimov, Bulat ; Likar, Bostjan ; Pernus, Franjo ; Vrtovec, Tomaz
Author_Institution :
Lab. of Imaging Technol., Univ. of Ljubljana, Ljubljana, Slovenia
Volume :
34
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
1649
Lastpage :
1662
Abstract :
Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.
Keywords :
bone; computerised tomography; image segmentation; interpolation; medical image processing; neurophysiology; object detection; optimisation; physiological models; 3D CT image spatial resolution; Dice coefficient; automated spinal structure segmentation; automated spine interpolation-based detection; automated vertebrae interpolation-based detection; automated vertebral structure segmentation; computed tomography; distance 0.3 mm; distance 1.1 mm; improved shape-constrained deformable model approach; optimization technique; signal-to-noise ratio; spinal column; thoracolumbar vertebrae; Computed tomography; Image segmentation; Interpolation; Optimization; Polynomials; Shape; Three-dimensional displays; Computed tomography; deformable models; image segmentation; interpolation theory; object detection; spine; vertebra;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2015.2389334
Filename :
7004869
Link To Document :
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